Drastic Anomaly Detection in Video Using Motion Direction Statistics
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概要
- 論文の詳細を見る
A novel approach for detecting anomaly in visual surveillance system is proposed in this paper. It is composed of three parts: (a) a dense motion field and motion statistics method, (b) motion directional PCA for feature dimensionality reduction, (c) an improved one-class SVM for one-class classification. Experiments demonstrate the effectiveness of the proposed algorithm in detecting abnormal events in surveillance video, while keeping a low false alarm rate. Our scheme works well in complicated situations that common tracking or detection modules cannot handle.
- （社）電子情報通信学会の論文
- 2011-08-01
著者
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Liu Chang
Department Of Electronic Engineering Tsinghua University
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Wang Guijin
Dept. Of Electronic Engineering Tsinghua University
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Lin Xinggang
Department Of Electronic Engineering Tsinghua University
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LIU Chang
the Department of Electronic Engineering, Tsinghua University
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WANG Guijin
the Department of Electronic Engineering, Tsinghua University
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LIN Xinggang
the Department of Electronic Engineering, Tsinghua University
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NING Wenxin
the Department of Electronic Engineering, Tsinghua University
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Liu Chang
The Department Of Electronic Engineering Tsinghua University
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Ning Wenxin
The Department Of Electronic Engineering Tsinghua University
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